A Random Extension for Discriminative Dimensionality Reduction and Metric Learning

被引：0

作者：

Perez-Suay, Adrian ^{[1
]}

Ferri, Francesc J. ^{[1
]}

Albert, Jesus V. ^{[1
]}

机构：

[1] Univ Valencia, Dept Informat, E-46003 Valencia, Spain

来源：

PATTERN RECOGNITION AND IMAGE ANALYSIS, PROCEEDINGS | 2009年 / 5524卷

关键词：

RECOGNITION; EIGENFACES; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A recently proposed metric learning algorithm which enforces the optimal discrimination of the different classes is extended and empirically assessed using different kinds of publicly available data. The optimization problem is posed in terms of landmark points and then, a stochastic approach is followed in order to bypass some of the problems of the original algorithm. According to the results, both computational burden and generalization ability are improved while absolute performance results remain almost unchanged.

引用

页码：370 / 377

页数：8

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